SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D Convolutional Neural Networks. - Patrick Ferdinand Christ; Florian Ettlinger; Georgios Kaissis; Sebastian Schlecht; Freba Ahmaddy; Felix Grün; Alexander Valentinitsch; Seyed-Ahmad Ahmadi; Rickmer Braren; Bjoern H. Menze (ISBI, 2017)

Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks. - Patrick Ferdinand Christ; Florian Ettlinger; Felix Grün; Mohamed Ezzeldin A. Elshaer; Jana Lipková; Sebastian Schlecht; Freba Ahmaddy; Sunil Tatavarty; Marc Bickel; Patrick Bilic; Markus Rempfler; Felix Hofmann; et al. (CoRR, 2017)

The Liver Tumor Segmentation Benchmark (LiTS). - Patrick Bilic; Patrick Ferdinand Christ; Eugene Vorontsov; Grzegorz Chlebus; Hao Chen 011; Qi Dou 001; Chi-Wing Fu; Xiao Han; Pheng-Ann Heng; Jürgen Hesser; Samuel Kadoury; Tomasz K. Konopczynski; et al. (CoRR, 2019)

Efficient, high-performance pancreatic segmentation using multi-scale feature extraction. - Moritz Knolle; Georgios Kaissis; Friederike Jungmann; Sebastian Ziegelmayer; Daniel Sasse; Marcus R. Makowski; Daniel Rueckert; Rickmer Braren (CoRR, 2020)

Privacy-preserving medical image analysis. - Alexander Ziller; Jonathan Passerat-Palmbach; Théo Ryffel; Dmitrii Usynin; Andrew Trask; Ionésio Da Lima Costa Junior; Jason Mancuso; Marcus R. Makowski; Daniel Rueckert; Rickmer Braren; Georgios Kaissis (CoRR, 2020)

Secure, privacy-preserving and federated machine learning in medical imaging. - Georgios Kaissis; Marcus R. Makowski; Daniel Rueckert; Rickmer Braren (Nat. Mach. Intell., 2020)

Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI. - Felix Meissen; Georgios Kaissis; Daniel Rueckert (BrainLes@MICCAI (1), 2021)

U-Noise: Learnable Noise Masks for Interpretable Image Segmentation. - Teddy Koker; Fatemehsadat Mireshghallah; Tom Titcombe; Georgios Kaissis (ICIP, 2021)

Segmentation of Peripancreatic Arteries in Multispectral Computed Tomography Imaging. - Alina Dima; Johannes C. Paetzold; Friederike Jungmann; Tristan Lemke; Philipp Raffler; Georgios Kaissis; Daniel Rueckert; Rickmer Braren (MLMI@MICCAI, 2021)

RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting. - Benjamin Hou; Georgios Kaissis; Ronald M. Summers; Bernhard Kainz (MICCAI (7), 2021)

AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation. - Felix Meissen; Georgios Kaissis; Daniel Rueckert (MIDOG/MOOD/Learn2Reg@MICCAI, 2021)

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience. - Johannes C. Paetzold; Julian McGinnis; Suprosanna Shit; Ivan Ezhov; Paul Büschl; Chinmay Prabhakar; Anjany Sekuboyina; Mihail I. Todorov; Georgios Kaissis; Ali Ertürk; Stephan Günnemann; Bjoern H. Menze (NeurIPS Datasets and Benchmarks, 2021)

HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning. - Reza Nasirigerdeh; Reihaneh Torkzadehmahani; Julian O. Matschinske; Jan Baumbach; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Differentially private federated deep learning for multi-site medical image segmentation. - Alexander Ziller; Dmitrii Usynin; Nicolas Remerscheid; Moritz Knolle; Marcus R. Makowski; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation. - Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Kritika Prakash; Andrew Trask; Rickmer Braren; Marcus R. Makowski; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty. - Moritz Knolle; Alexander Ziller; Dmitrii Usynin; Rickmer Braren; Marcus R. Makowski; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph). - Johannes C. Paetzold; Julian McGinnis; Suprosanna Shit; Ivan Ezhov; Paul Büschl; Chinmay Prabhakar; Mihail I. Todorov; Anjany Sekuboyina; Georgios Kaissis; Ali Ertürk; Stephan Günnemann; Bjoern H. Menze (CoRR, 2021)

An automatic differentiation system for the age of differential privacy. - Dmitrii Usynin; Alexander Ziller; Moritz Knolle; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Partial sensitivity analysis in differential privacy. - Tamara T. Mueller; Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Friederike Jungmann; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Complex-valued deep learning with differential privacy. - Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Kerstin Hammernik; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Distributed Machine Learning and the Semblance of Trust. - Dmitrii Usynin; Alexander Ziller; Daniel Rueckert; Jonathan Passerat-Palmbach; Georgios Kaissis (CoRR, 2021)

AI reflections in 2020. - Anna Jobin; Kingson Man; Antonio Damasio; Georgios Kaissis; Rickmer Braren; Julia Stoyanovich; Jay J. Van Bavel; Tessa V. West; Brent D. Mittelstadt 002; Jason Eshraghian; Marta R. Costa-jussà; Asaf Tzachor; et al. (Nat. Mach. Intell., 2021)

End-to-end privacy preserving deep learning on multi-institutional medical imaging. - Georgios Kaissis; Alexander Ziller; Jonathan Passerat-Palmbach; Théo Ryffel; Dmitrii Usynin; Andrew Trask; Ionésio Lima; Jason Mancuso; Friederike Jungmann; Marc-Matthias Steinborn; Andreas Saleh; Marcus R. Makowski; et al. (Nat. Mach. Intell., 2021)

Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. - Dmitrii Usynin; Alexander Ziller; Marcus R. Makowski; Rickmer Braren; Daniel Rueckert; Ben Glocker; Georgios Kaissis; Jonathan Passerat-Palmbach (Nat. Mach. Intell., 2021)

Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. - Qi Dou 001; Tiffany Y. So; Meirui Jiang; Quande Liu; Varut Vardhanabhuti; Georgios Kaissis; Zeju Li; Weixin Si; Heather H. C. Lee; Kevin Yu; Zuxin Feng; Li Dong; et al. (npj Digit. Medicine, 2021)

Unsupervised Anomaly Localization with Structural Feature-Autoencoders. - Felix Meissen; Johannes C. Paetzold; Georgios Kaissis; Daniel Rueckert (BrainLes@MICCAI, 2022)

Joint Learning of Localized Representations from Medical Images and Reports. - Philip Müller; Georgios Kaissis; Congyu Zou; Daniel Rueckert (ECCV (26), 2022)

Relationformer: A Unified Framework for Image-to-Graph Generation. - Suprosanna Shit; Rajat Koner; Bastian Wittmann; Johannes C. Paetzold; Ivan Ezhov; Hongwei Li 004; Jiazhen Pan; Sahand Sharifzadeh; Georgios Kaissis; Volker Tresp; Bjoern H. Menze (ECCV (37), 2022)

Multi-Modal Unsupervised Brain Image Registration Using Edge Maps. - Vasiliki Sideri-Lampretsa; Georgios Kaissis; Daniel Rueckert (ISBI, 2022)

Radiological Reports Improve Pre-training for Localized Imaging Tasks on Chest X-Rays. - Philip Müller; Georgios Kaissis; Congyu Zou; Daniel Rueckert (MICCAI (5), 2022)

Can Collaborative Learning Be Private, Robust and Scalable? - Dmitrii Usynin; Helena Klause; Johannes C. Paetzold; Daniel Rueckert; Georgios Kaissis (DeCaF/FAIR@MICCAI, 2022)

On the Pitfalls of Using the Residual Error as Anomaly Score. - Felix Meissen; Benedikt Wiestler; Georgios Kaissis; Daniel Rueckert (MIDL, 2022)

Differentially Private Graph Classification with GNNs. - Tamara T. Mueller; Johannes C. Paetzold; Chinmay Prabhakar; Dmitrii Usynin; Daniel Rueckert; Georgios Kaissis (CoRR, 2022)

Differentially private training of residual networks with scale normalisation. - Helena Klause; Alexander Ziller; Daniel Rueckert; Kerstin Hammernik; Georgios Kaissis (CoRR, 2022)

SoK: Differential Privacy on Graph-Structured Data. - Tamara T. Mueller; Dmitrii Usynin; Johannes C. Paetzold; Daniel Rueckert; Georgios Kaissis (CoRR, 2022)

Can collaborative learning be private, robust and scalable? - Dmitrii Usynin; Helena Klause; Daniel Rueckert; Georgios Kaissis (CoRR, 2022)

SmoothNets: Optimizing CNN architecture design for differentially private deep learning. - Nicolas W. Remerscheid; Alexander Ziller; Daniel Rueckert; Georgios Kaissis (CoRR, 2022)

Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis. - Florian A. Hölzl; Daniel Rueckert; Georgios Kaissis (CoRR, 2022)

Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens. - Georgios Kaissis; Alexander Ziller; Stefan Kolek Martinez de Azagra; Daniel Rueckert (CoRR, 2022)

The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images. - Philip Müller; Georgios Kaissis; Daniel Rueckert (CoRR, 2022)

How Do Input Attributes Impact the Privacy Loss in Differential Privacy? - Tamara T. Mueller; Stefan Kolek; Friederike Jungmann; Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Daniel Rueckert; Georgios Kaissis (CoRR, 2022)

Privacy: An Axiomatic Approach. - Alexander Ziller; Tamara T. Mueller; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (Entropy, 2022)

Unified Interpretation of the Gaussian Mechanism for Differential Privacy Through the Sensitivity Index. - Georgios Kaissis; Moritz Knolle; Friederike Jungmann; Alexander Ziller; Dmitrii Usynin; Daniel Rueckert (J. Priv. Confidentiality, 2022)

Author Correction: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. - Qi Dou 001; Tiffany Y. So; Meirui Jiang; Quande Liu; Varut Vardhanabhuti; Georgios Kaissis; Zeju Li; Weixin Si; Heather H. C. Lee; Kevin Yu; Zuxin Feng; Li Dong; et al. (npj Digit. Medicine, 2022)

Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning. - Dmitrii Usynin; Daniel Rueckert; Jonathan Passerat-Palmbach; Georgios Kaissis (Proc. Priv. Enhancing Technol., 2022)

Diagnosis. - Daniel Rueckert; Moritz Knolle; Nicolas Duchateau; Reza Razavi; Georgios Kaissis (AI and Big Data in Cardiology, 2023)

Membership Inference Attacks Against Semantic Segmentation Models. - Tomás Chobola; Dmitrii Usynin; Georgios Kaissis (AISec@CCS, 2023)

Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models. - Florian A. Hölzl; Daniel Rueckert; Georgios Kaissis (AISec@CCS, 2023)

Utility-preserving Federated Learning. - Reza Nasirigerdeh; Daniel Rueckert; Georgios Kaissis (AISec@CCS, 2023)

Interactive and Explainable Region-guided Radiology Report Generation. - Tim Tanida; Philip Müller; Georgios Kaissis; Daniel Rueckert (CVPR, 2023)

Exploiting Segmentation Labels and Representation Learning to Forecast Therapy Response of PDAC Patients. - Alexander Ziller; Ayhan Can Erdur; Friederike Jungmann; Daniel Rueckert; Rickmer Braren; Georgios Kaissis (ISBI, 2023)

Gradient Self-alignment in Private Deep Learning. - David Bani-Harouni; Tamara T. Mueller; Daniel Rueckert; Georgios Kaissis (ISIC/Care-AI/MedAGI/DeCaF@MICCAI, 2023)

Propagation and Attribution of Uncertainty in Medical Imaging Pipelines. - Leonhard F. Feiner; Martin J. Menten; Kerstin Hammernik; Paul Hager; Wenqi Huang 003; Daniel Rueckert; Rickmer F. Braren; Georgios Kaissis (UNSURE@MICCAI, 2023)

Extended Graph Assessment Metrics for Regression and Weighted Graphs. - Tamara T. Mueller; Sophie Starck; Leonhard F. Feiner; Kyriaki-Margarita Bintsi; Daniel Rueckert; Georgios Kaissis (GRAIL/OCELOT@MICCAI, 2023)

Body Fat Estimation from Surface Meshes Using Graph Neural Networks. - Tamara T. Mueller; Siyu Zhou; Sophie Starck; Friederike Jungmann; Alexander Ziller; Orhun Aksoy; Danylo Movchan; Rickmer Braren; Georgios Kaissis; Daniel Rueckert (ShapeMI@MICCAI, 2023)

Anatomy-Driven Pathology Detection on Chest X-rays. - Philip Müller; Felix Meissen; Johannes Brandt; Georgios Kaissis; Daniel Rueckert (MICCAI (1), 2023)

MAD: Modality Agnostic Distance Measure for Image Registration. - Vasiliki Sideri-Lampretsa; Veronika A. Zimmer; Huaqi Qiu; Georgios Kaissis; Daniel Rueckert (MTSAIL/LEAF/AI4Treat/MMMI/REMIA@MICCAI, 2023)

Robust Detection Outcome: A Metric for Pathology Detection in Medical Images. - Felix Meissen; Philip Müller; Georgios Kaissis; Daniel Rueckert (MIDL, 2023)

Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning. - Georgios Kaissis; Alexander Ziller; Stefan Kolek; Anneliese Riess; Daniel Rueckert (NeurIPS, 2023)

Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning. - Reza Nasirigerdeh; Javad Torkzadehmahani; Daniel Rueckert; Georgios Kaissis (SaTML, 2023)

Equivariant Differentially Private Deep Learning. - Florian A. Hölzl; Daniel Rueckert; Georgios Kaissis (CoRR, 2023)

Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology. - Soroosh Tayebi Arasteh; Alexander Ziller; Christiane Kuhl; Marcus R. Makowski; Sven Nebelung; Rickmer Braren; Daniel Rueckert; Daniel Truhn; Georgios Kaissis (CoRR, 2023)

Leveraging gradient-derived metrics for data selection and valuation in differentially private training. - Dmitrii Usynin; Daniel Rueckert; Georgios Kaissis (CoRR, 2023)

Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy. - Soroosh Tayebi Arasteh; Mahshad Lotfinia; Teresa Nolte; Marwin Saehn; Peter Isfort; Christiane Kuhl; Sven Nebelung; Georgios Kaissis; Daniel Truhn (CoRR, 2023)

Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy. - Georgios Kaissis; Jamie Hayes; Alexander Ziller; Daniel Rueckert (CoRR, 2023)

Explainable 2D Vision Models for 3D Medical Data. - Alexander Ziller; Alp Güvenir; Ayhan Can Erdur; Tamara T. Mueller; Philip Müller; Friederike Jungmann; Johannes Brandt; Jan Peeken; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (CoRR, 2023)

Privacy-Utility Trade-offs in Neural Networks for Medical Population Graphs: Insights from Differential Privacy and Graph Structure. - Tamara T. Mueller; Maulik Chevli; Ameya Daigavane; Daniel Rueckert; Georgios Kaissis (CoRR, 2023)

Extended Graph Assessment Metrics for Graph Neural Networks. - Tamara T. Mueller; Sophie Starck; Leonhard F. Feiner; Kyriaki-Margarita Bintsi; Daniel Rueckert; Georgios Kaissis (CoRR, 2023)

Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD. - Moritz Knolle; Robert Dorfman; Alexander Ziller; Daniel Rueckert; Georgios Kaissis (CoRR, 2023)

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare. - Karim Lekadir; Aasa Feragen; Abdul Joseph Fofanah; Alejandro F. Frangi; Alena Buyx; Anais Emelie; Andrea Lara; Antonio R. Porras; An-Wen Chan; Arcadi Navarro; Ben Glocker; Benard Ohene Botwe; et al. (CoRR, 2023)

(Predictable) Performance Bias in Unsupervised Anomaly Detection. - Felix Meissen; Svenja Breuer; Moritz Knolle; Alena Buyx; Ruth Müller; Georgios Kaissis; Benedikt Wiestler; Daniel Rueckert (CoRR, 2023)

SoK: Memorisation in machine learning. - Dmitrii Usynin; Moritz Knolle; Georgios Kaissis (CoRR, 2023)

How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection. - Felix Meissen; Johannes Getzner; Alexander Ziller; Georgios Kaissis; Daniel Rueckert (CoRR, 2023)

Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging. - Alexander Ziller; Tamara T. Mueller; Simon Stieger; Leonhard F. Feiner; Johannes Brandt; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (CoRR, 2023)

The Liver Tumor Segmentation Benchmark (LiTS). - Patrick Bilic; Patrick Ferdinand Christ; Hongwei Li 004; Eugene Vorontsov; Avi Ben-Cohen; Georgios Kaissis; Adi Szeskin; Colin Jacobs; Gabriel Efrain Humpire Mamani; Gabriel Chartrand; Fabian Lohöfer; Julian Walter Holch; et al. (Medical Image Anal., 2023)

Label Noise-Robust Learning using a Confidence-Based Sieving Strategy. - Reihaneh Torkzadehmahani; Reza Nasirigerdeh; Daniel Rueckert; Georgios Kaissis (Trans. Mach. Learn. Res., 2023)

Differentially Private Graph Neural Networks for Whole-Graph Classification. - Tamara T. Mueller; Johannes C. Paetzold; Chinmay Prabhakar; Dmitrii Usynin; Daniel Rueckert; Georgios Kaissis (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks. - Dmitrii Usynin; Daniel Rueckert; Georgios Kaissis (ACM Trans. Priv. Secur., 2023)

ChEX: Interactive Localization and Region Description in Chest X-Rays. - Philip Müller; Georgios Kaissis; Daniel Rueckert (ECCV (21), 2024)

Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training. - Dmitrii Usynin; Daniel Rueckert; Georgios Kaissis (EICC, 2024)

Beyond the Calibration Point: Mechanism Comparison in Differential Privacy. - Georgios Kaissis; Stefan Kolek; Borja Balle; Jamie Hayes; Daniel Rueckert (ICML, 2024)

Differentially Private Graph Neural Networks for Medical Population Graphs and The Impact of The Graph Structure. - Tamara T. Mueller; Maulik Chevli; Ameya Daigavane; Daniel Rueckert; Georgios Kaissis (ISBI, 2024)

On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models. - Deniz Daum; Richard Osuala; Anneliese Riess; Georgios Kaissis; Julia A. Schnabel; Maxime Di Folco (DGM4MICCAI@MICCAI, 2024)

Fair and Private CT Contrast Agent Detection. - Philipp Kaess; Alexander Ziller; Lea Mantz; Daniel Rueckert; Florian J. Fintelmann; Georgios Kaissis (FAIMI/EPIMI@MICCAI, 2024)

Enhancing the Utility of Privacy-Preserving Cancer Classification Using Synthetic Data. - Richard Osuala; Daniel Lang 003; Anneliese Riess; Georgios Kaissis; Zuzanna Szafranowska; Grzegorz Skorupko; Oliver Díaz; Julia A. Schnabel; Karim Lekadir (Deep-Breath@MICCAI, 2024)

Complex-Valued Federated Learning with Differential Privacy and MRI Applications. - Anneliese Riess; Alexander Ziller; Stefan Kolek; Daniel Rueckert; Julia A. Schnabel; Georgios Kaissis (ISIC/iMIMIC/EARTH/DeCaF@MICCAI, 2024)

Attack-Aware Noise Calibration for Differential Privacy. - Bogdan Kulynych; Juan Felipe Gómez; Georgios Kaissis; Flávio P. Calmon; Carmela Troncoso (NeurIPS, 2024)

Bounding Reconstruction Attack Success of Adversaries Without Data Priors. - Alexander Ziller; Anneliese Riess; Kristian Schwethelm; Tamara T. Mueller; Daniel Rueckert; Georgios Kaissis (CoRR, 2024)

Visual Privacy Auditing with Diffusion Models. - Kristian Schwethelm; Johannes Kaiser; Moritz Knolle; Daniel Rueckert; Georgios Kaissis; Alexander Ziller (CoRR, 2024)

Machine Unlearning for Medical Imaging. - Reza Nasirigerdeh; Nader Razmi; Julia A. Schnabel; Daniel Rueckert; Georgios Kaissis (CoRR, 2024)

Improved Localized Machine Unlearning Through the Lens of Memorization. - Reihaneh Torkzadehmahani; Reza Nasirigerdeh; Georgios Kaissis; Daniel Rueckert; Gintare Karolina Dziugaite; Eleni Triantafillou (CoRR, 2024)

Differentially Private Guarantees for Analytics and Machine Learning on Graphs: A Survey of Results. - Tamara T. Mueller; Dmitrii Usynin; Johannes C. Paetzold; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (J. Priv. Confidentiality, 2024)

Federated learning is not a cure-all for data ethics. - Marieke A. R. Bak; Vince I. Madai; Leo Anthony Celi; Georgios Kaissis; Ronald Cornet; Menno Maris; Daniel Rueckert; Alena Buyx; Stuart McLennan (Nat. Mac. Intell., 2024)

Reconciling privacy and accuracy in AI for medical imaging. - Alexander Ziller; Tamara T. Mueller; Simon Stieger; Leonhard F. Feiner; Johannes Brandt; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (Nat. Mac. Intell., 2024)

A Survey on Graph Construction for Geometric Deep Learning in Medicine: Methods and Recommendations. - Tamara T. Mueller; Sophie Starck; Alina Dima; Stephan Wunderlich; Kyriaki-Margarita Bintsi; Kamilia Zaripova; Rickmer Braren; Daniel Rueckert; Anees Kazi; Georgios Kaissis (Trans. Mach. Learn. Res., 2024)

Are Population Graphs Really as Powerful as Believed? - Tamara T. Müller; Sophie Starck; Kyriaki-Margarita Bintsi; Alexander Ziller; Rickmer Braren; Georgios Kaissis; Daniel Rueckert (Trans. Mach. Learn. Res., 2024)

Kernel Normalized Convolutional Networks. - Reza Nasirigerdeh; Reihaneh Torkzadehmahani; Daniel Rueckert; Georgios Kaissis (Trans. Mach. Learn. Res., 2024)

Memorisation in Machine Learning: A Survey of Results. - Dmitrii Usynin; Moritz Knolle; Georgios Kaissis (Trans. Mach. Learn. Res., 2024)

Unsupervised Pathology Detection: A Deep Dive Into the State of the Art. - Ioannis Lagogiannis; Felix Meissen; Georgios Kaissis; Daniel Rueckert (IEEE Trans. Medical Imaging, 2024)

Encrypted federated learning for secure decentralized collaboration in cancer image analysis. - Daniel Truhn; Soroosh Tayebi Arasteh; Oliver Lester Saldanha; Gustav Müller-Franzes; Firas Khader; Philip Quirke; Nicholas P. West; Richard Gray 005; Gordon G. A. Hutchins; Jacqueline A. James; Maurice B. Loughrey; Manuel Salto-Tellez; et al. (Medical Image Anal., 2024)

Laplace Sample Information: Data Informativeness Through a Bayesian Lens. - Johannes Kaiser; Kristian Schwethelm; Daniel Rueckert; Georgios Kaissis (ICLR, 2025)

Differentially Private Active Learning: Balancing Effective Data Selection and Privacy. - Kristian Schwethelm; Johannes Kaiser; Jonas Kuntzer; Mehmet Yigitsoy; Daniel Rückert; Georgios Kaissis (SaTML, 2025)

Cross-Domain and Cross-Dimension Learning for Image-to-Graph Transformers. - Alexander H. Berger; Laurin Lux; Suprosanna Shit; Ivan Ezhov; Georgios Kaissis; Martin J. Menten; Daniel Rueckert; Johannes C. Paetzold (WACV, 2025)

Scaling Laws for Differentially Private Language Models. - Ryan McKenna; Yangsibo Huang; Amer Sinha; Borja Balle; Zachary Charles; Christopher A. Choquette-Choo; Badih Ghazi; Georgios Kaissis; Ravi Kumar 001; Ruibo Liu; Da Yu; Chiyuan Zhang (CoRR, 2025)

Weakly Supervised Object Detection in Chest X-Rays With Differentiable ROI Proposal Networks and Soft ROI Pooling. - Philip Müller; Felix Meissen; Georgios Kaissis; Daniel Rueckert (IEEE Trans. Medical Imaging, 2025)

(ϵ, δ) Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees. - Juan Felipe Gómez; Bogdan Kulynych; Georgios Kaissis; Jamie Hayes; Borja Balle; Antti Honkela (CoRR, 2025)

Redirection for Erasing Memory (REM): Towards a universal unlearning method for corrupted data. - Stefan Schoepf; Michael Curtis Mozer; Nicole Mitchell; Alexandra Brintrup; Georgios Kaissis; Peter Kairouz; Eleni Triantafillou (CoRR, 2025)

Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models. - Jamie Hayes; Ilia Shumailov; Christopher A. Choquette-Choo; Matthew Jagielski; Georgios Kaissis; Katherine Lee; Milad Nasr; Sahra Ghalebikesabi; Niloofar Mireshghallah; Meenatchi Sundaram Mutu Selva Annamalai; Igor Shilov; Matthieu Meeus; et al. (CoRR, 2025)

The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing. - Meenatchi Sundaram Muthu Selva Annamalai; Borja Balle; Jamie Hayes; Georgios Kaissis; Emiliano De Cristofaro (CoRR, 2025)

Counterfactual Influence as a Distributional Quantity. - Matthieu Meeus; Igor Shilov; Georgios Kaissis; Yves-Alexandre de Montjoye (CoRR, 2025)

Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy. - Bogdan Kulynych; Juan Felipe Gómez; Georgios Kaissis; Jamie Hayes; Borja Balle; Flávio P. Calmon; Jean Louis Raisaro (CoRR, 2025)

On Arbitrary Predictions from Equally Valid Models. - Sarah Lockfisch; Kristian Schwethelm; Martin J. Menten; Rickmer Braren; Daniel Rueckert; Alexander Ziller; Georgios Kaissis (CoRR, 2025)

Beyond Benchmarks: Dynamic, Automatic And Systematic Red-Teaming Agents For Trustworthy Medical Language Models. - Jiazhen Pan; Bailiang Jian; Paul Hager; Yundi Zhang; Che Liu; Friederike Jungmann; Hongwei Bran Li; Chenyu You; Junde Wu; Jiayuan Zhu; Fenglin Liu; Yuyuan Liu; et al. (CoRR, 2025)